用深度神经网络生成拟人步态，实时调整脚步防摔倒。
Online DNN-driven Nonlinear MPC for Stylistic Humanoid Robot Walking with Step Adjustment
- 用自回归DNN生成初始步态，模仿人类动作
- 在线优化确保动态稳定，抗扰动达68牛
- 结合卡尔曼滤波与遗传算法提升控制精度
本文提出一种三层架构，实现具有在线脚位调整能力的风格化行走。方法结合自回归深度神经网络（DNN）作为轨迹生成层，与基于模型的轨迹调整和控制层。DNN利用人体运动捕捉数据训练，输出质心与姿态参考，作为后续两层的初始猜测与正则化。轨迹调整层采用非线性优化，在保证质心动态可行性的同时实现步态调整。对比了两种实现方式：滚动时域规划（RHP）与模型预测控制（MPC）。为提升MPC性能，引入卡尔曼滤波降噪，并通过遗传算法自动调参。在ergoCub人形机器人上的实验表明，系统可有效防止跌倒，复现人类步态风格，并承受高达68牛的外部扰动。
原文摘要 · Abstract (English)
This paper presents a three-layered architecture that enables stylistic locomotion with online contact location adjustment. Our method combines an autoregressive Deep Neural Network (DNN) acting as a trajectory generation layer with a model-based trajectory adjustment and trajectory control layers. The DNN produces centroidal and postural references serving as an initial guess and regularizer for the other layers. Being the DNN trained on human motion capture data, the resulting robot motion exhibits locomotion patterns, resembling a human walking style. The trajectory adjustment layer utilizes non-linear optimization to ensure dynamically feasible center of mass (CoM) motion while addressing step adjustments. We compare two implementations of the trajectory adjustment layer: one as a receding horizon planner (RHP) and the other as a model predictive controller (MPC). To enhance MPC performance, we introduce a Kalman filter to reduce measurement noise. The filter parameters are automatically tuned with a Genetic Algorithm. Experimental results on the ergoCub humanoid robot demonstrate the system's ability to prevent falls, replicate human walking styles, and withstand disturbances up to 68 Newton. Website: https://sites.google.com/view/dnn-mpc-walking Youtube video: https://www.youtube.com/watch?v=x3tzEfxO-xQ
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